Customer Data Platform · Developer Tools
buryhuang/mcp-hubspot
A Model Context Protocol (MCP) server that enables AI assistants to interact with HubSpot CRM data. This server bridges AI models with your HubSpot account, providing direct access to contacts, companies, and engagement data. Built-in vector storage and caching mechanisms help overcome HubSpot API limitations while improving response times.
docker run -e HUBSPOT_ACCESS_TOKEN=<value> buryhuang/mcp-hubspot:latestOur implementation prioritizes the most frequently used, high-value HubSpot operations with robust error handling and API stability. Each component is optimized for AI-friendly interactions, ensuring reliable performance even during complex, multi-step CRM workflows.
Vector Storage: Utilizes FAISS for efficient semantic search and retrieval
Thread-Level Indexing: Stores each conversation thread individually for precise retrieval
Embedding Caching: Uses SentenceTransformer with automatic caching
Persistent Storage: Data persists between sessions in configurable storage directory
Multi-platform Support: Optimized Docker images for various architectures
hubspot_create_contact
Create contacts with duplicate prevention
hubspot_create_company
Create companies with duplicate prevention
hubspot_get_company_activity
Retrieve activity for specific companies
hubspot_get_active_companies
Retrieve most recently active companies
hubspot_get_active_contacts
Retrieve most recently active contacts
hubspot_get_recent_conversations
Retrieve recent conversation threads with messages
hubspot_search_data
Semantic search across previously retrieved HubSpot data
Details on this page are taken from the project's README. Open README
peakmojo/mcp-hubspot
A Model Context Protocol (MCP) server that enables AI assistants to interact with HubSpot CRM data. This server bridges AI models with your HubSpot account, providing direct access to contacts, companies, and engagement data. Built-in vector storage and caching mechanisms help overcome HubSpot API limitations while improving response times.
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